{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 实验考试复习"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1、安装\n",
    "Python的安装较为简单，只需要根据系统类型下载对应的Python安装包即可\n",
    "\n",
    "![image.png](install.png)\n",
    "\n",
    "一般的来说勾选上了Add Python to path就可以选择安装了，不需要对高级设置进行选择。勾选Add to PATH来加入环境变量，方便我们以后在终端或者命令行提示符中使用Python\n",
    "\n",
    "![image.png](lenthlimit.png)\n",
    "\n",
    "最后如果出现这个提示请点击一下这个提示，这个提示是用于解除windows下的最大地址长度限制的，这在我们以后使用pip安装软件的时候非常有用，如果在这里不勾选，可能以后使用pip安装的软件会出现莫名奇妙的bug\n",
    "\n",
    "安装完Python以后第一件事就是更换pip的软件源，因为Python官方的软件源仓库在中国的访问速度很慢，所以我们需要将其更换为国内的镜像软件源，这里可以使用\n",
    "\n",
    "```bash\n",
    "pip config set global.index-url https://mirrors.cqupt.edu.cn/pypi/simple/\n",
    "```\n",
    "来更换pip的源为重庆邮电大学源\n",
    "\n",
    "Python软件包的安装十分简单，一般安装软件包采用`pip`命令，然后输入**你要的指令**，然后加入包的名字，例如:\n",
    "```bash\n",
    "pip install numpy\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2、导入包\n",
    "Python的导入包一般有两种语法，一种是`import xx`，还有一种是`from xx import xx`，它们的区别如下：\\\n",
    "`import xx`就是将xx下的**所有的包都导入进来**，但是如果要使用，就需要带上我们导入的包的名字并在后面**加**`.`来进行链式调用,这样**包的导入方式就很清晰**，具体方法知道是由什么包导入进来的，例如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy # 导入Numpy这个软件包\n",
    "numpy.array([1,2,3]) # 链式调用numpy包内部的类或者方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当然我们也可以使用`as`为**导入的包起一个别名**，例如:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x27d1dad0088>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt # 为matplotlib.pyplot这个导入的包起一个别名\n",
    "import numpy as np # 为Numpy导入起一个别名\n",
    "plt.plot(np.arange(0,5,0.1),np.arange(0,5,0.1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "而`from xx import xx`则是将**某个包下的特定包导入进来**，它并不会把整个Python包导入进来，只会导入我们指定的包，这样更具体，而且不易造成命名空间冲突的问题，例如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5, 1.4, 0.2],\n",
       "       [4.9, 3. , 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2],\n",
       "       [5.4, 3.9, 1.7, 0.4],\n",
       "       [4.6, 3.4, 1.4, 0.3],\n",
       "       [5. , 3.4, 1.5, 0.2],\n",
       "       [4.4, 2.9, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import datasets # 指明需要导入的包是sklearn下的datasets\n",
    "iris=datasets.load_iris()\n",
    "iris.data[:10] # 鸢尾花数据的前十行记录"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3、导入数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一般的来说，"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4、Python的线性代数计算"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5、Numpy常见操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6、matplotlib绘制3维等高线图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7、sklearn导入机器学习模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8、数据挖掘算法"
   ]
  }
 ],
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